To denoise implicit feedback, some recent efforts have been done by using re-sampling methods [1]}, [2]}, [3]}, [4]} or re-weighting methods [5]}.
Re-sampling methods focus on designing more effective samplers. For example, [2]} considers that the missing interactions of popular items are highly likely to be real negative examples. However, the performance of re-sampling methods depends heavily on the sampling distribution and suffers from high variance [7]}. [5]} proposed a re-weighting method which assigns lower weights or zero weights to high-loss samples since the noisy examples would have higher losses. However, [9]} shows that hard yet clean examples also tend to have high losses. As a result, [5]} could encounter difficulties to distinguish between hard clean and noisy examples. Besides, some research focuses on utilizing auxiliary information to denoise implicit feedback [11]}, [12]}, [13]}, [14]} but this kinds of methods need additional input.
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